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        <span>nlp_crf模型</span>
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        <h1 id="nlp-crf模型"><a href="#nlp-crf模型" class="headerlink" title="nlp_crf模型"></a>nlp_crf模型</h1><h2 id="sklearn-crfsuite的安装"><a href="#sklearn-crfsuite的安装" class="headerlink" title="sklearn-crfsuite的安装"></a>sklearn-crfsuite的安装</h2><p>直接pip install sklearn_crfsuite即可</p>
<h2 id="总结一些链接"><a href="#总结一些链接" class="headerlink" title="总结一些链接"></a>总结一些链接</h2><p>对sklearn_crfsuite的一些相关介绍 <a target="_blank" rel="noopener" href="https://zhuanlan.zhihu.com/p/74408364">https://zhuanlan.zhihu.com/p/74408364</a> </p>
<p>基于 CRF 的中文命名实体识别模型实现 <a target="_blank" rel="noopener" href="https://www.jianshu.com/p/7fa260e91382">https://www.jianshu.com/p/7fa260e91382</a> </p>
<p>NLP关键词提取方法总结及实现 <a target="_blank" rel="noopener" href="https://blog.csdn.net/asialee_bird/article/details/96454544">https://blog.csdn.net/asialee_bird/article/details/96454544</a> </p>
<p>使用CRF++实现命名实体识别(NER) <a target="_blank" rel="noopener" href="https://blog.csdn.net/BF02jgtRS00XKtCx/article/details/93806235?utm_medium=distribute.pc_relevant_download.none-task-blog-baidujs-4.nonecase&amp;depth_1-utm_source=distribute.pc_relevant_download.none-task-blog-baidujs-4.nonecase">https://blog.csdn.net/BF02jgtRS00XKtCx/article/details/93806235?utm_medium=distribute.pc_relevant_download.none-task-blog-baidujs-4.nonecase&amp;depth_1-utm_source=distribute.pc_relevant_download.none-task-blog-baidujs-4.nonecase</a> </p>
<p>基于gensim TFIDF模型 的文章推荐算法 <a target="_blank" rel="noopener" href="https://blog.csdn.net/qq_34333481/article/details/85327090?utm_medium=distribute.pc_aggpage_search_result.none-task-blog-2~all~first_rank_v2~rank_v25-5-85327090.nonecase&utm_term=gensim%E4%BF%9D%E5%AD%98%E6%A8%A1%E5%9E%8B">https://blog.csdn.net/qq_34333481/article/details/85327090?utm_medium=distribute.pc_aggpage_search_result.none-task-blog-2<del>all</del>first_rank_v2~rank_v25-5-85327090.nonecase&amp;utm_term=gensim%E4%BF%9D%E5%AD%98%E6%A8%A1%E5%9E%8B</a> </p>
<p>标注训练——crfsuite <a target="_blank" rel="noopener" href="https://www.jianshu.com/p/613ea47e98e7">https://www.jianshu.com/p/613ea47e98e7</a> </p>
<p>基于crf的CoNLL2002数据集命名实体识别模型实现-pycrfsuite <a target="_blank" rel="noopener" href="https://blog.csdn.net/leitouguan8655/article/details/83382412">https://blog.csdn.net/leitouguan8655/article/details/83382412</a> </p>
<p>Let’s use CoNLL 2002 data to build a NER system [<a target="_blank" rel="noopener" href="https://nbviewer.jupyter.org/github/tpeng/python-crfsuite/blob/master/examples/CoNLL%202002.ipynb]">https://nbviewer.jupyter.org/github/tpeng/python-crfsuite/blob/master/examples/CoNLL%202002.ipynb]</a>(<a target="_blank" rel="noopener" href="https://nbviewer.jupyter.org/github/tpeng/python-crfsuite/blob/master/examples/CoNLL">https://nbviewer.jupyter.org/github/tpeng/python-crfsuite/blob/master/examples/CoNLL</a> 2002.ipynb) </p>
<h3 id="Tutorial官网对CRF的使用"><a href="#Tutorial官网对CRF的使用" class="headerlink" title="Tutorial官网对CRF的使用"></a>Tutorial官网对CRF的使用</h3><p> <a target="_blank" rel="noopener" href="https://sklearn-crfsuite.readthedocs.io/en/latest/tutorial.html#">https://sklearn-crfsuite.readthedocs.io/en/latest/tutorial.html#</a> </p>
<h2 id="姓名语料建立"><a href="#姓名语料建立" class="headerlink" title="姓名语料建立"></a>姓名语料建立</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/9/23</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> gensim <span class="keyword">import</span> corpora, models, similarities</span><br><span class="line"><span class="keyword">import</span> en_core_web_md</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pickle</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">nlp = en_core_web_md.load()</span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取姓名</span></span><br><span class="line">f = <span class="built_in">open</span>(<span class="string">r&#x27;en_name.txt&#x27;</span>, <span class="string">&#x27;r&#x27;</span>)</span><br><span class="line">result = []</span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> f.readlines():</span><br><span class="line">    result.append(<span class="built_in">list</span>(<span class="built_in">map</span>(<span class="built_in">str</span>, line[:<span class="number">-1</span>].split(<span class="string">&#x27;,&#x27;</span>))))</span><br><span class="line">f.close()</span><br><span class="line">result.pop(<span class="number">0</span>)</span><br><span class="line">result.insert(<span class="number">0</span>, [<span class="string">&#x27;aadi&#x27;</span>])</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 变成小写去重</span></span><br><span class="line">data = [result[i][<span class="number">0</span>].lower() <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(result))]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 将其转换为词典</span></span><br><span class="line">doc = [[text] <span class="keyword">for</span> text <span class="keyword">in</span> <span class="built_in">list</span>(<span class="built_in">set</span>(data))]</span><br><span class="line"></span><br><span class="line">dictionary = corpora.Dictionary(doc)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 保存生成的词典</span></span><br><span class="line">dictionary.save(<span class="string">&#x27;train_dictionary.dict&#x27;</span>)</span><br><span class="line"></span><br><span class="line">corpus = [dictionary.doc2bow(text) <span class="keyword">for</span> text <span class="keyword">in</span> doc]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 将语料转换为tf-idf的索引</span></span><br><span class="line">tfidf_model = models.TfidfModel(corpus)[corpus]</span><br><span class="line"></span><br><span class="line">tfidf_model.save(<span class="string">&#x27;train_tfidf.model&#x27;</span>)</span><br><span class="line"></span><br><span class="line">index = similarities.MatrixSimilarity(tfidf_model)</span><br><span class="line"></span><br><span class="line">index.save(<span class="string">&#x27;train_index.index&#x27;</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<h2 id="提取中文英文姓名"><a href="#提取中文英文姓名" class="headerlink" title="提取中文英文姓名"></a>提取中文英文姓名</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br><span class="line">108</span><br><span class="line">109</span><br><span class="line">110</span><br><span class="line">111</span><br><span class="line">112</span><br><span class="line">113</span><br><span class="line">114</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/9/23</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> gensim <span class="keyword">import</span> corpora, models, similarities</span><br><span class="line"><span class="keyword">import</span> en_core_web_md</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">nlp = en_core_web_md.load()</span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取姓名</span></span><br><span class="line">f = <span class="built_in">open</span>(<span class="string">r&#x27;en_name.txt&#x27;</span>, <span class="string">&#x27;r&#x27;</span>)</span><br><span class="line">result = []</span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> f.readlines():</span><br><span class="line">    result.append(<span class="built_in">list</span>(<span class="built_in">map</span>(<span class="built_in">str</span>, line[:<span class="number">-1</span>].split(<span class="string">&#x27;,&#x27;</span>))))</span><br><span class="line">f.close()</span><br><span class="line">result.pop(<span class="number">0</span>)</span><br><span class="line">result.insert(<span class="number">0</span>, [<span class="string">&#x27;aadi&#x27;</span>])</span><br><span class="line"></span><br><span class="line"><span class="comment"># 变成小写</span></span><br><span class="line">data = [result[i][<span class="number">0</span>].lower() <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(result))]</span><br><span class="line"><span class="comment"># 去除里面的停靠词</span></span><br><span class="line">df = [word <span class="keyword">for</span> word <span class="keyword">in</span> data <span class="keyword">if</span> <span class="keyword">not</span> nlp.vocab[word].is_stop != <span class="literal">False</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取中文姓名</span></span><br><span class="line">cn_name = np.array(pd.read_excel(<span class="string">&quot;cn_name.xlsx&quot;</span>, header=<span class="literal">None</span>)[<span class="number">1</span>][<span class="number">0</span>:<span class="number">100</span>]).tolist()</span><br><span class="line"></span><br><span class="line">ccname = <span class="built_in">list</span>(<span class="built_in">set</span>(cn_name))</span><br><span class="line">all_name = ccname + df</span><br><span class="line"></span><br><span class="line"><span class="comment"># # 将其转换为词典</span></span><br><span class="line">dictionary = corpora.Dictionary([[text] <span class="keyword">for</span> text <span class="keyword">in</span> all_name])</span><br><span class="line">corpus = [dictionary.doc2bow([text]) <span class="keyword">for</span> text <span class="keyword">in</span> all_name]</span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="comment"># # 将语料转换为tf-idf的索引</span></span><br><span class="line">tfidf_model = models.TfidfModel(corpus)[corpus]</span><br><span class="line">index = similarities.MatrixSimilarity(tfidf_model)</span><br><span class="line"></span><br><span class="line">stop_words = [<span class="string">&#x27;,&#x27;</span>, <span class="string">&#x27;.&#x27;</span>, <span class="string">&#x27;[&#x27;</span>, <span class="string">&#x27;]&#x27;</span>, <span class="string">&#x27;;&#x27;</span>, <span class="string">&#x27;(&#x27;</span>, <span class="string">&#x27;)&#x27;</span>, <span class="string">&#x27;&#123;&#x27;</span>, <span class="string">&#x27;&#125;&#x27;</span>]</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">del_stop</span>(<span class="params">lines</span>):</span></span><br><span class="line">    token_doc = [token.lemma_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(lines.lower())]</span><br><span class="line">    key_word = []</span><br><span class="line">    <span class="keyword">for</span> word <span class="keyword">in</span> token_doc:</span><br><span class="line">        <span class="keyword">if</span> word <span class="keyword">not</span> <span class="keyword">in</span> stop_words:</span><br><span class="line">            key_word.append(word)</span><br><span class="line">    <span class="keyword">return</span> key_word</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">input_data = pd.read_excel(<span class="string">&quot;data.xlsx&quot;</span>, header=<span class="literal">None</span>)</span><br><span class="line">df = np.array(input_data).tolist()</span><br><span class="line">symbol = np.array(pd.read_excel(<span class="string">&quot;标志词.xlsx&quot;</span>, header=<span class="literal">None</span>)[<span class="number">0</span>]).tolist()</span><br><span class="line">time = [<span class="string">&#x27;year&#x27;</span>, <span class="string">&#x27;day&#x27;</span>, <span class="string">&#x27;minute&#x27;</span>, <span class="string">&#x27;second&#x27;</span>, <span class="string">&#x27;today&#x27;</span>, <span class="string">&#x27;tomorrow&#x27;</span>, <span class="string">&#x27;yesterday&#x27;</span>, <span class="string">&#x27;morning&#x27;</span>, <span class="string">&#x27;bainoon&#x27;</span>, <span class="string">&#x27;afternoon&#x27;</span>,</span><br><span class="line">        <span class="string">&#x27;evening&#x27;</span>, <span class="string">&#x27;tonight&#x27;</span>, <span class="string">&#x27;night&#x27;</span>]</span><br><span class="line">x = [<span class="string">&#x27;%s&#x27;</span> % i <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">2022</span>)]</span><br><span class="line">jie = [<span class="string">&#x27;in&#x27;</span>, <span class="string">&#x27;as&#x27;</span>, <span class="string">&#x27;at&#x27;</span>, <span class="string">&#x27;the&#x27;</span>]</span><br><span class="line"></span><br><span class="line">years = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">203</span>):</span><br><span class="line">    c = <span class="number">10</span> * i</span><br><span class="line">    years.append(<span class="string">&#x27;%s&#x27;</span> % c + <span class="string">&#x27;s&#x27;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">label = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(df)):</span><br><span class="line">    keys = del_stop(df[i][<span class="number">0</span>])</span><br><span class="line">    num = <span class="number">0</span></span><br><span class="line">    <span class="keyword">for</span> words <span class="keyword">in</span> keys:</span><br><span class="line">        <span class="keyword">if</span> words <span class="keyword">not</span> <span class="keyword">in</span> symbol:</span><br><span class="line">            <span class="keyword">pass</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            num += <span class="number">1</span></span><br><span class="line">    <span class="keyword">if</span> num &gt; <span class="number">0</span>:</span><br><span class="line">        label.append(<span class="number">1</span>)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        label.append(<span class="number">0</span>)</span><br><span class="line">    <span class="comment"># if num &gt; 0:</span></span><br><span class="line">    <span class="comment">#     num1 = 0</span></span><br><span class="line">    <span class="comment">#     num2 = 0</span></span><br><span class="line">    <span class="comment">#     for index in range(len(keys)):</span></span><br><span class="line">    <span class="comment">#         if keys[index] not in symbol:</span></span><br><span class="line">    <span class="comment">#             num1 = 0</span></span><br><span class="line">    <span class="comment">#         elif keys[index] not in time:</span></span><br><span class="line">    <span class="comment">#             num1 = 0</span></span><br><span class="line">    <span class="comment">#         elif keys[index] not in years:</span></span><br><span class="line">    <span class="comment">#             num1 = 0</span></span><br><span class="line">    <span class="comment">#         elif keys[index] not in x:</span></span><br><span class="line">    <span class="comment">#             num1 = 0</span></span><br><span class="line">    <span class="comment">#         elif keys[index] in x:</span></span><br><span class="line">    <span class="comment">#             if keys[index - 1] not in jie:</span></span><br><span class="line">    <span class="comment">#                 num1 = 0</span></span><br><span class="line">    <span class="comment">#         else:</span></span><br><span class="line">    <span class="comment">#             num2 += 1</span></span><br><span class="line">    <span class="comment">#     if num2 &gt; 0:</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 重新处理需要去除停靠词</span></span><br><span class="line"></span><br><span class="line">token_doc = [token.lemma_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(df[<span class="number">0</span>][<span class="number">0</span>].lower())]</span><br><span class="line"></span><br><span class="line">text_tfidf = dictionary.doc2bow(del_stop(df[<span class="number">3</span>][<span class="number">0</span>]))</span><br><span class="line">similar = index[text_tfidf]</span><br><span class="line">dict_score = <span class="built_in">dict</span>(<span class="built_in">zip</span>(np.arange(<span class="built_in">len</span>(all_name)), similar))</span><br><span class="line">sort_score = <span class="built_in">sorted</span>(<span class="built_in">zip</span>(dict_score.values(), dict_score.keys()), reverse=<span class="literal">True</span>)</span><br><span class="line">print(sort_score[<span class="number">0</span>][<span class="number">0</span>])</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">5</span>):</span><br><span class="line">    print(all_name[sort_score[i][<span class="number">1</span>]])</span><br><span class="line"></span><br></pre></td></tr></table></figure>

<h2 id="提取语句对其进行姓名判断"><a href="#提取语句对其进行姓名判断" class="headerlink" title="提取语句对其进行姓名判断"></a>提取语句对其进行姓名判断</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br><span class="line">97</span><br><span class="line">98</span><br><span class="line">99</span><br><span class="line">100</span><br><span class="line">101</span><br><span class="line">102</span><br><span class="line">103</span><br><span class="line">104</span><br><span class="line">105</span><br><span class="line">106</span><br><span class="line">107</span><br><span class="line">108</span><br><span class="line">109</span><br><span class="line">110</span><br><span class="line">111</span><br><span class="line">112</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/9/23</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> gensim <span class="keyword">import</span> corpora, models, similarities</span><br><span class="line"><span class="keyword">import</span> en_core_web_md</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">nlp = en_core_web_md.load()</span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 读取姓名</span></span><br><span class="line">f = <span class="built_in">open</span>(<span class="string">r&#x27;en_name.txt&#x27;</span>, <span class="string">&#x27;r&#x27;</span>)</span><br><span class="line">result = []</span><br><span class="line"><span class="keyword">for</span> line <span class="keyword">in</span> f.readlines():</span><br><span class="line">    result.append(<span class="built_in">list</span>(<span class="built_in">map</span>(<span class="built_in">str</span>, line[:<span class="number">-1</span>].split(<span class="string">&#x27;,&#x27;</span>))))</span><br><span class="line">f.close()</span><br><span class="line">result.pop(<span class="number">0</span>)</span><br><span class="line">result.insert(<span class="number">0</span>, [<span class="string">&#x27;aadi&#x27;</span>])</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 变成小写去重</span></span><br><span class="line">data = [result[i][<span class="number">0</span>].lower() <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(result))]</span><br><span class="line"></span><br><span class="line"><span class="comment"># # 将其转换为词典</span></span><br><span class="line">dictionary = corpora.Dictionary([[text] <span class="keyword">for</span> text <span class="keyword">in</span> data])</span><br><span class="line">corpus = [dictionary.doc2bow([text]) <span class="keyword">for</span> text <span class="keyword">in</span> data]</span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="comment"># # 将语料转换为tf-idf的索引</span></span><br><span class="line">tfidf_model = models.TfidfModel(corpus)[corpus]</span><br><span class="line">index = similarities.MatrixSimilarity(tfidf_model)</span><br><span class="line"><span class="comment">#</span></span><br><span class="line">stop_words = [<span class="string">&#x27;,&#x27;</span>, <span class="string">&#x27;.&#x27;</span>, <span class="string">&#x27;[&#x27;</span>, <span class="string">&#x27;]&#x27;</span>, <span class="string">&#x27;;&#x27;</span>, <span class="string">&#x27;(&#x27;</span>, <span class="string">&#x27;)&#x27;</span>, <span class="string">&#x27;&#123;&#x27;</span>, <span class="string">&#x27;&#125;&#x27;</span>]</span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="comment">#</span></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">del_stop</span>(<span class="params">lines</span>):</span></span><br><span class="line">    token_doc = [token.lemma_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(lines.lower())]</span><br><span class="line">    key_word = []</span><br><span class="line">    <span class="keyword">for</span> word <span class="keyword">in</span> token_doc:</span><br><span class="line">        <span class="keyword">if</span> word <span class="keyword">not</span> <span class="keyword">in</span> stop_words:</span><br><span class="line">            key_word.append(word)</span><br><span class="line">    <span class="keyword">return</span> key_word</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 对数据进行处理</span></span><br><span class="line">data = pd.read_excel(<span class="string">&quot;data.xlsx&quot;</span>, header=<span class="literal">None</span>)</span><br><span class="line">df = np.array(data).tolist()</span><br><span class="line"><span class="comment">#</span></span><br><span class="line">keys = del_stop(df[<span class="number">0</span>][<span class="number">0</span>])</span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 判断标志词</span></span><br><span class="line">symbol = np.array(pd.read_excel(<span class="string">&quot;标志词.xlsx&quot;</span>, header=<span class="literal">None</span>)[<span class="number">0</span>]).tolist()</span><br><span class="line"></span><br><span class="line">num = []</span><br><span class="line"><span class="keyword">for</span> words <span class="keyword">in</span> keys:</span><br><span class="line">    <span class="keyword">if</span> words <span class="keyword">not</span> <span class="keyword">in</span> symbol:</span><br><span class="line">        <span class="keyword">pass</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        num.append(words)</span><br><span class="line">        print(<span class="string">f&#x27;有标志词&#x27;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 判断是否有时间</span></span><br><span class="line"></span><br><span class="line">time = [<span class="string">&#x27;year&#x27;</span>, <span class="string">&#x27;day&#x27;</span>, <span class="string">&#x27;minute&#x27;</span>, <span class="string">&#x27;second&#x27;</span>, <span class="string">&#x27;today&#x27;</span>, <span class="string">&#x27;tomorrow&#x27;</span>, <span class="string">&#x27;yesterday&#x27;</span>, <span class="string">&#x27;morning&#x27;</span>, <span class="string">&#x27;bainoon&#x27;</span>, <span class="string">&#x27;afternoon&#x27;</span>,</span><br><span class="line">        <span class="string">&#x27;evening&#x27;</span>, <span class="string">&#x27;tonight&#x27;</span>, <span class="string">&#x27;night&#x27;</span>]</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">x = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">2022</span>):</span><br><span class="line">    x.append(<span class="string">&#x27;%s&#x27;</span> % i)</span><br><span class="line"></span><br><span class="line">jie = [<span class="string">&#x27;in&#x27;</span>, <span class="string">&#x27;as&#x27;</span>, <span class="string">&#x27;at&#x27;</span>, <span class="string">&#x27;the&#x27;</span>]</span><br><span class="line">years = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">203</span>):</span><br><span class="line">    c = <span class="number">10</span> * i</span><br><span class="line">    years.append(<span class="string">&#x27;%s&#x27;</span> % c + <span class="string">&#x27;s&#x27;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">num = <span class="number">0</span></span><br><span class="line">num1 = <span class="number">0</span></span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(keys)):</span><br><span class="line">    <span class="keyword">if</span> keys[i] <span class="keyword">not</span> <span class="keyword">in</span> symbol:</span><br><span class="line">        num = <span class="number">0</span></span><br><span class="line">    <span class="keyword">elif</span> keys[i] <span class="keyword">not</span> <span class="keyword">in</span> time:</span><br><span class="line">        num = <span class="number">0</span></span><br><span class="line">    <span class="keyword">elif</span> keys[i] <span class="keyword">not</span> <span class="keyword">in</span> years:</span><br><span class="line">        num = <span class="number">0</span></span><br><span class="line">    <span class="keyword">elif</span> keys[i] <span class="keyword">not</span> <span class="keyword">in</span> x:</span><br><span class="line">        num = <span class="number">0</span></span><br><span class="line">    <span class="keyword">elif</span> keys[i] <span class="keyword">in</span> x:</span><br><span class="line">        <span class="keyword">if</span> keys[i<span class="number">-1</span>] <span class="keyword">not</span> <span class="keyword">in</span> jie:</span><br><span class="line">            num = <span class="number">0</span></span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        num1 += <span class="number">1</span></span><br><span class="line"></span><br><span class="line"><span class="keyword">if</span> num1 &gt; <span class="number">0</span>:</span><br><span class="line">    print(<span class="string">&#x27;有时间判断&#x27;</span>)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 判断人名</span></span><br><span class="line"></span><br><span class="line">text_tfidf = dictionary.doc2bow(keys)</span><br><span class="line">similar = index[text_tfidf]</span><br><span class="line"></span><br><span class="line">dict_score = <span class="built_in">dict</span>(<span class="built_in">zip</span>(np.arange(<span class="built_in">len</span>(result)), similar))</span><br><span class="line"></span><br><span class="line">sort_score = <span class="built_in">sorted</span>(<span class="built_in">zip</span>(dict_score.values(), dict_score.keys()), reverse=<span class="literal">True</span>)</span><br></pre></td></tr></table></figure>

<h2 id="特征标签"><a href="#特征标签" class="headerlink" title="特征标签"></a>特征标签</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br><span class="line">96</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/9/24</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> en_core_web_md</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">nlp = en_core_web_md.load()</span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">1. 只需要管里面是否存在PROPN即可</span></span><br><span class="line"><span class="string">2. 直接调用spacy进行处理。但是需要进行去除停靠词这些</span></span><br><span class="line"><span class="string">3. 相当于处理了姓名，然后还有一个是symbol标志词</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"></span><br><span class="line">stop_words = [<span class="string">&#x27;,&#x27;</span>, <span class="string">&#x27;.&#x27;</span>, <span class="string">&#x27;[&#x27;</span>, <span class="string">&#x27;]&#x27;</span>, <span class="string">&#x27;;&#x27;</span>, <span class="string">&#x27;&#123;&#x27;</span>, <span class="string">&#x27;&#125;&#x27;</span>]</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">del_stop</span>(<span class="params">lines</span>):</span></span><br><span class="line">    token_doc = [token.lemma_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(lines.lower())]</span><br><span class="line">    key_word = []</span><br><span class="line">    <span class="keyword">for</span> word <span class="keyword">in</span> token_doc:</span><br><span class="line">        <span class="keyword">if</span> word <span class="keyword">not</span> <span class="keyword">in</span> stop_words:</span><br><span class="line">            key_word.append(word)</span><br><span class="line">    <span class="keyword">return</span> key_word</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 先判定是否存在姓名</span></span><br><span class="line"><span class="comment"># 读取语料</span></span><br><span class="line">input_data = np.array(pd.read_excel(<span class="string">&quot;data.xlsx&quot;</span>, header=<span class="literal">None</span>)[<span class="number">0</span>]).tolist()</span><br><span class="line">symbol = np.array(pd.read_excel(<span class="string">&quot;标志词.xlsx&quot;</span>, header=<span class="literal">None</span>)[<span class="number">0</span>]).tolist()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.进行pos判断，看是否存在人名</span></span><br><span class="line">pos = [[token.pos_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(word.lower())] <span class="keyword">for</span> word <span class="keyword">in</span> input_data]</span><br><span class="line">name_pos = []</span><br><span class="line"><span class="keyword">for</span> text <span class="keyword">in</span> pos:</span><br><span class="line">    num = <span class="string">&#x27;O&#x27;</span></span><br><span class="line">    <span class="keyword">if</span> <span class="string">&#x27;PROPN&#x27;</span> <span class="keyword">in</span> text:</span><br><span class="line">        num = <span class="string">&#x27;B-PER&#x27;</span></span><br><span class="line">    name_pos.append(num)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 对symbol进行处理</span></span><br><span class="line">symbol_featrue = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(input_data)):</span><br><span class="line">    keys = del_stop(input_data[i])</span><br><span class="line">    num_s = <span class="number">0</span></span><br><span class="line">    <span class="keyword">for</span> words <span class="keyword">in</span> keys:</span><br><span class="line">        <span class="keyword">if</span> words <span class="keyword">not</span> <span class="keyword">in</span> symbol:</span><br><span class="line">            <span class="keyword">pass</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            num_s += <span class="number">1</span></span><br><span class="line">    <span class="keyword">if</span> num_s &gt; <span class="number">0</span>:</span><br><span class="line">        symbol_featrue.append(<span class="string">&#x27;B-SY&#x27;</span>)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        symbol_featrue.append(<span class="string">&#x27;O&#x27;</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4. 判断时间是否在里面,创建时间组合</span></span><br><span class="line">ttt = [<span class="string">&#x27;%s&#x27;</span> % i <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">2022</span>)]</span><br><span class="line">jie = [<span class="string">&#x27;in&#x27;</span>, <span class="string">&#x27;as&#x27;</span>, <span class="string">&#x27;at&#x27;</span>, <span class="string">&#x27;the&#x27;</span>, <span class="string">&#x27;(&#x27;</span>, <span class="string">&#x27;to&#x27;</span>, <span class="string">&#x27;by&#x27;</span>]</span><br><span class="line">years = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">203</span>):</span><br><span class="line">    c = <span class="number">10</span> * i</span><br><span class="line">    years.append(<span class="string">&#x27;%s&#x27;</span> % c + <span class="string">&#x27;s&#x27;</span>)</span><br><span class="line">years = years + [<span class="string">&#x27;year&#x27;</span>, <span class="string">&#x27;years&#x27;</span>]</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 给一个时间组合标签</span></span><br><span class="line">time_feature = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(input_data)):</span><br><span class="line">    keys1 = del_stop(input_data[i])</span><br><span class="line">    num_time = <span class="number">0</span></span><br><span class="line">    <span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(keys1)):</span><br><span class="line">        <span class="keyword">if</span> keys1[index] <span class="keyword">in</span> years:</span><br><span class="line">            num_time += <span class="number">1</span></span><br><span class="line">        <span class="keyword">elif</span> keys1[index] <span class="keyword">in</span> ttt:</span><br><span class="line">            <span class="keyword">if</span> keys1[index<span class="number">-1</span>] <span class="keyword">in</span> jie:</span><br><span class="line">                num_time += <span class="number">1</span></span><br><span class="line">    <span class="keyword">if</span> num_time &gt; <span class="number">0</span>:</span><br><span class="line">        time_feature.append(<span class="string">&#x27;B-TI&#x27;</span>)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        time_feature.append(<span class="string">&#x27;O&#x27;</span>)</span><br><span class="line"></span><br><span class="line">feature_name = pd.DataFrame(name_pos, columns=&#123;<span class="string">&#x27;name&#x27;</span>&#125;)</span><br><span class="line">feature_symbol = pd.DataFrame(symbol_featrue, columns=&#123;<span class="string">&#x27;symbol&#x27;</span>&#125;)</span><br><span class="line">feature_time = pd.DataFrame(time_feature, columns=&#123;<span class="string">&#x27;time&#x27;</span>&#125;)</span><br><span class="line"></span><br><span class="line">all_feature = pd.concat([feature_name, feature_symbol, feature_time], axis=<span class="number">1</span>)</span><br><span class="line"></span><br><span class="line">all_feature.to_excel(<span class="string">&quot;feature_NP.xlsx&quot;</span>)</span><br><span class="line"></span><br><span class="line"></span><br></pre></td></tr></table></figure>



<h2 id="用spacy进行语料处理"><a href="#用spacy进行语料处理" class="headerlink" title="用spacy进行语料处理"></a>用spacy进行语料处理</h2><figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br><span class="line">73</span><br><span class="line">74</span><br><span class="line">75</span><br><span class="line">76</span><br><span class="line">77</span><br><span class="line">78</span><br><span class="line">79</span><br><span class="line">80</span><br><span class="line">81</span><br><span class="line">82</span><br><span class="line">83</span><br><span class="line">84</span><br><span class="line">85</span><br><span class="line">86</span><br><span class="line">87</span><br><span class="line">88</span><br><span class="line">89</span><br><span class="line">90</span><br><span class="line">91</span><br><span class="line">92</span><br><span class="line">93</span><br><span class="line">94</span><br><span class="line">95</span><br></pre></td><td class="code"><pre><span class="line"><span class="comment"># -*- coding: utf-8 -*-</span></span><br><span class="line"><span class="comment"># @Time     : 2020/9/24</span></span><br><span class="line"><span class="comment"># @Author   : esy</span></span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">import</span> en_core_web_md</span><br><span class="line"><span class="keyword">import</span> warnings</span><br><span class="line"></span><br><span class="line"></span><br><span class="line">nlp = en_core_web_md.load()</span><br><span class="line">warnings.filterwarnings(<span class="string">&quot;ignore&quot;</span>)</span><br><span class="line"></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"><span class="string">1. 只需要管里面是否存在PROPN即可</span></span><br><span class="line"><span class="string">2. 直接调用spacy进行处理。但是需要进行去除停靠词这些</span></span><br><span class="line"><span class="string">3. 相当于处理了姓名，然后还有一个是symbol标志词</span></span><br><span class="line"><span class="string">&quot;&quot;&quot;</span></span><br><span class="line"></span><br><span class="line">stop_words = [<span class="string">&#x27;,&#x27;</span>, <span class="string">&#x27;.&#x27;</span>, <span class="string">&#x27;[&#x27;</span>, <span class="string">&#x27;]&#x27;</span>, <span class="string">&#x27;;&#x27;</span>, <span class="string">&#x27;&#123;&#x27;</span>, <span class="string">&#x27;&#125;&#x27;</span>]</span><br><span class="line"></span><br><span class="line"><span class="function"><span class="keyword">def</span> <span class="title">del_stop</span>(<span class="params">lines</span>):</span></span><br><span class="line">    token_doc = [token.lemma_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(lines.lower())]</span><br><span class="line">    key_word = []</span><br><span class="line">    <span class="keyword">for</span> word <span class="keyword">in</span> token_doc:</span><br><span class="line">        <span class="keyword">if</span> word <span class="keyword">not</span> <span class="keyword">in</span> stop_words:</span><br><span class="line">            key_word.append(word)</span><br><span class="line">    <span class="keyword">return</span> key_word</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 1. 先判定是否存在姓名</span></span><br><span class="line"><span class="comment"># 读取语料</span></span><br><span class="line">input_data = np.array(pd.read_excel(<span class="string">&quot;data.xlsx&quot;</span>, header=<span class="literal">None</span>)[<span class="number">0</span>]).tolist()</span><br><span class="line">symbol = np.array(pd.read_excel(<span class="string">&quot;标志词.xlsx&quot;</span>, header=<span class="literal">None</span>)[<span class="number">0</span>]).tolist()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.进行pos判断，看是否存在人名</span></span><br><span class="line">pos = [[token.pos_ <span class="keyword">for</span> token <span class="keyword">in</span> nlp(word.lower())] <span class="keyword">for</span> word <span class="keyword">in</span> input_data]</span><br><span class="line">name_pos = []</span><br><span class="line"><span class="keyword">for</span> text <span class="keyword">in</span> pos:</span><br><span class="line">    num = <span class="number">0</span></span><br><span class="line">    <span class="keyword">if</span> <span class="string">&#x27;PROPN&#x27;</span> <span class="keyword">in</span> text:</span><br><span class="line">        num = <span class="number">1</span></span><br><span class="line">    name_pos.append(num)</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 3. 对symbol进行处理</span></span><br><span class="line">symbol_featrue = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(input_data)):</span><br><span class="line">    keys = del_stop(input_data[i])</span><br><span class="line">    num_s = <span class="number">0</span></span><br><span class="line">    <span class="keyword">for</span> words <span class="keyword">in</span> keys:</span><br><span class="line">        <span class="keyword">if</span> words <span class="keyword">not</span> <span class="keyword">in</span> symbol:</span><br><span class="line">            <span class="keyword">pass</span></span><br><span class="line">        <span class="keyword">else</span>:</span><br><span class="line">            num_s += <span class="number">1</span></span><br><span class="line">    <span class="keyword">if</span> num_s &gt; <span class="number">0</span>:</span><br><span class="line">        symbol_featrue.append(<span class="number">1</span>)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        symbol_featrue.append(<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 4. 判断时间是否在里面,创建时间组合</span></span><br><span class="line">ttt = [<span class="string">&#x27;%s&#x27;</span> % i <span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">2022</span>)]</span><br><span class="line">jie = [<span class="string">&#x27;in&#x27;</span>, <span class="string">&#x27;as&#x27;</span>, <span class="string">&#x27;at&#x27;</span>, <span class="string">&#x27;the&#x27;</span>, <span class="string">&#x27;(&#x27;</span>, <span class="string">&#x27;to&#x27;</span>, <span class="string">&#x27;by&#x27;</span>]</span><br><span class="line">years = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="number">1</span>, <span class="number">203</span>):</span><br><span class="line">    c = <span class="number">10</span> * i</span><br><span class="line">    years.append(<span class="string">&#x27;%s&#x27;</span> % c + <span class="string">&#x27;s&#x27;</span>)</span><br><span class="line">years = years + [<span class="string">&#x27;year&#x27;</span>, <span class="string">&#x27;years&#x27;</span>]</span><br><span class="line"></span><br><span class="line"></span><br><span class="line"><span class="comment"># 给一个时间组合标签</span></span><br><span class="line">time_feature = []</span><br><span class="line"><span class="keyword">for</span> i <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(input_data)):</span><br><span class="line">    keys1 = del_stop(input_data[i])</span><br><span class="line">    num_time = <span class="number">0</span></span><br><span class="line">    <span class="keyword">for</span> index <span class="keyword">in</span> <span class="built_in">range</span>(<span class="built_in">len</span>(keys1)):</span><br><span class="line">        <span class="keyword">if</span> keys1[index] <span class="keyword">in</span> years:</span><br><span class="line">            num_time += <span class="number">1</span></span><br><span class="line">        <span class="keyword">elif</span> keys1[index] <span class="keyword">in</span> ttt:</span><br><span class="line">            <span class="keyword">if</span> keys1[index<span class="number">-1</span>] <span class="keyword">in</span> jie:</span><br><span class="line">                num_time += <span class="number">1</span></span><br><span class="line">    <span class="keyword">if</span> num_time &gt; <span class="number">0</span>:</span><br><span class="line">        time_feature.append(<span class="number">1</span>)</span><br><span class="line">    <span class="keyword">else</span>:</span><br><span class="line">        time_feature.append(<span class="number">0</span>)</span><br><span class="line"></span><br><span class="line">feature_name = pd.DataFrame(name_pos, columns=&#123;<span class="string">&#x27;name&#x27;</span>&#125;)</span><br><span class="line">feature_symbol = pd.DataFrame(symbol_featrue, columns=&#123;<span class="string">&#x27;symbol&#x27;</span>&#125;)</span><br><span class="line">feature_time = pd.DataFrame(time_feature, columns=&#123;<span class="string">&#x27;time&#x27;</span>&#125;)</span><br><span class="line"></span><br><span class="line">all_feature = pd.concat([feature_name, feature_symbol, feature_time], axis=<span class="number">1</span>)</span><br><span class="line"></span><br><span class="line">all_feature.to_excel(<span class="string">&quot;feature.xlsx&quot;</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>

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